activity
20232025
most citedTrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System

3 citations · 3 across the 8 of their papers we have counts for

collaborators

11 papers

cs.CV2025

Kwai Keye-VL 1.5 Technical Report

Biao Yang, Bin Wen, Boyang Ding +58

In recent years, the development of Large Language Models (LLMs) has significantly advanced, extending their capabilities to multimodal tasks through Multimodal Large Language Mode…

cs.CV2025

Thyme: Think Beyond Images

Yi-Fan Zhang, Xingyu Lu, Shukang Yin +17

Following OpenAI's introduction of the ``thinking with images'' concept, recent efforts have explored stimulating the use of visual information in the reasoning process to enhance…

cs.CV2025

Kwai Keye-VL Technical Report

Kwai Keye Team, Biao Yang, Bin Wen +57

While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form vi…

cs.IR2025

KLAN: Kuaishou Landing-page Adaptive Navigator

Fan Li, Chang Meng, Jiaqi Fu +5

Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the…

cs.AI2025

Why Distillation can Outperform Zero-RL: The Role of Flexible Reasoning

Xiao Hu, Xingyu Lu, Liyuan Mao +6

Reinforcement learning (RL) has played an important role in improving the reasoning ability of large language models (LLMs). Some studies apply RL directly to \textit{smaller} base…

cs.CV2025

R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning

Yi-Fan Zhang, Xingyu Lu, Xiao Hu +13

Multimodal Reward Models (MRMs) play a crucial role in enhancing the performance of Multimodal Large Language Models (MLLMs). While recent advancements have primarily focused on im…